Using computational modelling to understand the effects of pharmacological manipulations on cognitive function

这个合作项目是Jonathan Roiser教授（伦敦大学学院认知神经科学研究所）和剑桥认知有限公司的合作项目。罗瑟教授的团队专注于理解心理健康问题，尤其是抑郁症背后的认知和神经机制。在过去的5年里，他的工作越来越多地采用了计算方法，使用生成模型来更好地理解人类参与者执行认知任务时观察到的行为模式（和大脑反应）。剑桥认知公司在开发计算机化认知测试方面处于世界领先地位，它销售著名的剑桥神经心理测试自动化电池（CANTAB）软件。它经常与学术界合作开发计算机化测试，可以测量特定的认知领域，例如执行功能、记忆、注意力和决策。该公司开发的测试基于人类和动物的大量神经科学数据。对计算机化认知测试数据的分析是一个活跃的发展领域，在过去十年中，使用计算方法创建概括特定认知过程的参数变得越来越普遍。这种方法包括用数学的形式精确地说明实验者如何相信参与者正在完成任务。这被称为“生成模型”（因为，给定与人类执行相同的任务，该模型可以生成行为反应）。模型的行为受特定参数的控制，这些参数可以在认知框架中解释，并且通过将模型拟合到人类参与者收集的数据中，可以估计参数。与传统的描述性数据分析方法不同，这些参数可以作为汇总统计数据，而传统的描述性数据分析方法通常只涉及计算平均值或条件之间的差异。计算方法的主要优点是它可以利用数据固有的丰富性，这在传统方法中通常被忽视（例如，随着时间的推移逐渐演变的过程很难用纯粹的描述性方法捕获）。该项目的一个主要目标是为CANTAB测试开发计算模型，目前使用传统的描述方法对其进行分析。剑桥认知拥有大量从普通人群中收集的数据（包括面对面的和使用其在线平台的），这些数据可用于此目的。在开发模型之后，将进行实验，以了解模型参数如何受到心理健康问题（特别是抑郁症）症状和药理学干预（特别是增强认知能力的药物）的影响。

This collaborative project is a partnership between Prof Jonathan Roiser (UCL Institute of Cognitive Neuroscience) and Cambridge Cognition Ltd. Prof Roiser's group focuses on understanding the cognitive and neural mechanisms underpinning mental health problems, especially depression. Over the past 5 years his work increasingly adopted a computational approach, using generative models to better understand patterns of behaviour (and brain responses) observed when human participants perform cognitive tasks.Cambridge Cognition is a world leader in developing computerised cognitive testing, and it sells the well-known Cambridge Neuropsychological Test Automated Battery (CANTAB) software. It develops, often in partnership with academics, computerised tests that allow the measurement of specific domains of cognition, for example executive function, memory, attention and decision making. The tests it develops are based on a wealth of neuroscientific data from humans and animals.The analysis of data derived from computerised cognitive tests is an active area of development, and over the past decade it has become increasingly common to use a computational approach to create parameters that summarise specific cognitive processes. This approach involves specifying, in mathematical form, precisely how the experimenter believes that the participant is completing the task. This is known as a "generative model" (because, given the same task as the human would perform, the model can generate behavioural responses). The behaviour of the model is governed by specific parameters, which can be interpreted in a cognitive framework, and by fitting the model to data collected in human participants it is possible to estimate parameters. These parameters then serve as summary statistics, in contrast to the traditional descriptive approach to data analysis, which usually involves simply calculating means or differences between conditions. The major advantage of the computational approach is that it can capitalise on the richness inherent in the data, which is usually overlooked in the traditional approach (for example, processes that evolve gradually over time which are difficult to capture using a purely descriptive approach).A major aim of this project is to develop computational models for CANTAB tests, which are currently analysed using traditional descriptive approaches. Cambridge Cognition owns a wealth of data collected in the general population (both face-to-face and using its online platform) that can be used for this purpose. Following the development of the models, experiments will be conducted to understand how the model parameters are influenced by symptoms of mental health problems (especially depression), and by pharmacological interventions, particularly with cognitive enhancing drugs.